Abstract
With the considerable increase of visible satellites for positioning, the fault detection and identification performance of Range Consensus (RANCO) algorithm for Receiver Autonomous Integrity Monitoring (RAIM) will significantly be improved. However, the calculation amount of RANCO algorithm will exponentially increase for the sharp addition of visible satellite subsets. This paper proposes a modified RANCO algorithm based on genetic algorithm (GA-RANCO) for RAIM to inhibit the exponentially expanded calculation amount. To reduce the calculation amount in searching the optimal minimal necessary subset (MNS), the preselection step is developed to speed up the convergence process of GA-RANCO. It is executed to exclude the chromosome-represented MNS for which the count of faulty satellites will exceed the upper limit of independent simultaneous satellite faults to be monitored. Mathematical simulations are introduced to determine the GA parameters, and simulation experiments under different schemes are designed to evaluate the performance of GA-RANCO algorithm. Results illustrate that the time consumption under a large number of visible satellites of GA-RANCO is much lower than that of RANCO and the faulty detection and identification performance of GA-RANCO is the same as that of RANCO.
Highlights
Introduction e integrity ofGNSS is an important factor to ensure the safety of civil aviation, which can be assured at system or user level [1,2,3]. e former is provided through an independent network of monitoring stations and a dedicated integrity channel, such as satellite-based augmentation system (SBAS) and ground-based augmentation system (GBAS), and the latter is provided by Receiver Autonomous Integrity Monitoring (RAIM) [4, 5]
In this paper, considering that the essence of Range Consensus (RANCO) algorithm is to search the optimal minimal necessary subset (MNS), we propose a modified RANCO algorithm based on genetic algorithm (GA) for RAIM, which is a bionic optimization algorithm achieved by simulating the biological evolution process, including inheritance, preselection, selection, crossover, and mutation [26,27,28,29]. is modified algorithm, abbreviated as GA-RANCO, can inhibit the exponentially increasing calculation amount of RANCO
A modified RANCO algorithm based on GA is proposed to inhibit the exponentially expanded calculation amount
Summary
Assuming that the pseudorange noise of each visible satellite obeys zero mean normal distribution in nominal mode and the pseudorange observations are independent of each other, the RANCO algorithm is designed to identify multiple faulty satellites by searching outliers for the MNS, which contains the least visible satellites being able to estimate the user position. For the ith visible satellite under test of MNSj, denoted as SUTji , its pseudorange residual can be expressed as follows: ωji zi − hiX j,. Visible satellite ωji would obey in MNSj and SUTji normal distribution is in with the the following statistical characteristics:. When each visible mode while in SUTji is satellite a faulty in MNSj satellite, is ωji in the would nominal obey the following distribution:. Utilizing the property of SSEj+ , an indicator can be constructed to evaluate the performance for every MNS as follows: Tj. MNSj with the minimum Tj value will be considered as the optimal one, denoted as MNSopt. E LOS-RANCO approach to preexclude the poor geometry MNS before outlier identifying is implemented by calculating the correlation coefficient (CCF) of the LOS vectors for each two visible satellites. Given a threshold for CCF, the MNS should be excluded when the CCF value of any two satellites in it exceeds that threshold. e threshold for CCF is set as TCCF 0.8 [11]
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